B.E. | Artificial Intelligence and Machine Learning

Course Overview

1. What is Artificial Intelligence and Machine Learning specialization in Engineering?
Artificial Intelligence and Machine Learning is a branch of study or discipline which includes theories, standards, methods and innovations of various different domains like mathematics, cognitive science, electronics and embedded systems to make intelligent systems that mimic human behaviour. Artificial Intelligence and Machine Learning focuses on collecting, categorizing, strategizing, analyzing and interpretation of data. It is a specialised branch that deals with the development of embedded systems like robotics and IoT based applications. It also incorporates the concepts of machine learning and deep learning model building for solving various computational and real world business problems.

2. Who should study Artificial Intelligence and Machine Learning study?
Artificial Intelligence and Machine Learning is an appropriate course for those who like to develop various innovative and intelligence solutions to solve complex industrial and business problems. This can contribute in industrial automation, information technology and other sectors like healthcare, agriculture, wearable, space, and meteorology through analysis of raw data, extract intelligence from that and design, develop, support and testing of AI and ML based systems along with embedded applications.

3. What will I study in this course?
In this course, you will learn how to design, create and implement AI and ML based software solution to solve real-world problems. This course helps to explore concepts such as AI, Machine Learning, Deep Learning, Image Processing, Virtual Reality and IoT and its applications.

4. What are the career opportunities after the completion of this course?/What will I do once I graduate?
AI and ML graduates will be able to design, create and implement intelligent software applications to solve real-world business and industrial problems. They use the latest tools and open source technologies to recommend apt solutions. They can figure out how to evaluate the ethical, legitimate, proficient and social standards of engineering knowledge and practices. AI and ML graduates can also showcase their expertise in knowledge management, mobile and distributed application development, intelligence web/ecommerce development, database administration, computer hardware, networking, education and training and decision support systems using machine learning concepts with the help of latest tools and technologies.sdsd

Programme Duration

Programme Duration
4 years (8 semesters)

Programme Type

Eligibility Criteria

The candidate should have passed the 2nd PUC/12th/Equivalent Exam with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics along with Chemistry/Biotechnology/Biology/Electronics/Computers (40% for Karnataka reserved category candidates).

Candidate must also qualify in one of the following entrance exams: CET/ COMED-K/JEE/AIEEE

Course Structure

The AI and ML syllabus is as follows:

I & II Semester

  • Calculus and Linear Algebra
  • Engineering Physics
  • Basic Electrical Engineering
  • Elements of Civil Engineering and Mechanics
  • Engineering Graphics
  • Engineering Physics Laboratory
  • Basic Electrical Engineering Laboratory
  • Technical English-I
  • Engineering Chemistry
  • C Programming For Problem Solving
  • Basic Electronics
  • Elements of Mechanical Engineering
  • Engineering Chemistry Laboratory
  • C Programming Laboratory
  • Advanced Calculus and Numerical Methods
  • Technical English-II

III Semester

  • Transform Calculus, Fourier Series and Numerical Techniques
  • Data Structures and Applications
  • Analog and Digital Electronics
  • Computer Organization
  • Software Engineering
  • Discrete Mathematical Structures
  • Analog and Digital Electronics Laboratory
  • Data Structures Laboratory
  • Kannada/Constitution of India, Professional Ethics and Cyber Law

IV Semester

  • Complex Analysis, Probability and Statistical Methods
  • Design and Analysis of Algorithms
  • Operating Systems
  • Microcontroller and Embedded Systems
  • Object Oriented Concepts
  • Data Communication
  • Design and Analysis of Algorithms Laboratory
  • Microcontroller and Embedded Systems Laboratory
  • Kannada/Constitution of India, Professional Ethics and Cyber Law

V Semester

  • Management and Entrepreneurship for IT Industry
  • Automata Languages and Artificial Intelligence
  • Sensors and Sensor Applications
  • Machine Learning with Python
  • Computer Graphics and Image Processing
  • Database Management System
  • AI and ML with Python Laboratory
  • Database Laboratory
  • Environmental Studies

VI Semester

  • Advanced AI and ML techniques
  • Advanced Image Processing with Virtual Reality
  • Web applications using Machine Learning Techniques
  • Professional Elective -1
  • Open Elective -A
  • Natural Language and Image Processing Laboratory
  • Web Application Laboratory
  • Miniproject
  • Internship

VII Semester

  • Robotics process automation
  • Internet of Things with Machine Learning
  • Professional Elective 2
  • Professional Elective 3
  • Open Elective – B
  • Mobile Application development and Robotics automation Laboratory
  • Internet of Things Laboratory
  • Internship
  • Project work Phase I

VIII Semester

  • Big Data and its Applications
  • Professional Elective-4
  • Project Work Phase II
  • Technical Seminar
  • Internship


Students can choose from the following electives:


  • Advanced Computer Graphics with Virtual Reality
  • Soft and Evolutionary Computing
  • Decision Support System
  • Cryptography and Network Security
  • Data Mining and Warehousing


  • Introduction to Artificial Intelligence
  • Introduction to Soft computing
  • Introduction to web Technology


  • Speech Processing
  • Business Analytics
  • Cognitive Systems
  • Biometric Systems
  • Mobile Application Development


  • Pervasive Computing
  • Human Computer Interaction
  • Knowledge and Data Engineering
  • Cloud computing and Virtualization


  • Introduction to business intelligence
  • Introduction to Cloud computing
  • Introduction to Biometrics System


  • Artificial Intelligence and Machine Learning in Healthcare
  • System Modelling and Simulation
  • Artificial Intelligence in Agriculture
  • Multilayer neural Networks and Deep Learning

Evaluation Criteria


  • The Continuous Internal Evaluation (CIE) is prescribed for maximum of 40 marks. Marks prescribed for test shall be 30 and for assignment is 10. The CIE marks for test in a theory Course shall be based on three tests and generally conducted at the end of fifth, tenth and fourteenth week of each semester. Each test shall be conducted for a maximum of 30 marks and the final marks shall be the average of three tests. However, to support slow learners, improvement tests will be carried out to help them gain the average. The remaining 10 marks shall be awarded based on the evaluation of Assignments/ Unit Tests/ written quizzes that support to cover some of the Course/programme outcomes. Final CIE marks awarded shall be the sum of test marks and assignment marks making a maximum of 40 marks.
  • In the case of Practical, the CIE marks shall be based on the laboratory journals/records (30 marks for continuous evaluation based on conduct of experiment, viva and report writing and one practical test (10 marks) to be conducted at the end of the semester.
  • The IA marks in the case of Mini Project (in 5th Semester), Projects and Seminars in the final year shall be based on the evaluation at the end of 8th semester.


  • Assignments are given to students after completion of each unit of the syllabus and comprehensively cover all of the important aspects of each topic in a particular unit.
  • Completing the prescribed assignments will greatly help students prepare for the internal assessments and the final exams. All the assignments will be evaluated and based on the performance of the students marks will be awarded for each course.
  • The Student Assistant for the course will neatly script solutions to assignments, and after due checking and correction by faculty, these solutions will be scanned and made available on the faculty webpage for all students to access and download.

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